Computed Tomography Reconstruction Algorithm Using Markov Random Field Model

TL;DR

This paper evaluates a Bayesian CT reconstruction algorithm based on Markov random fields, demonstrating improved image quality over FBP under low-dose and sparse-view conditions, with adaptive hyperparameter estimation.

Contribution

The study introduces a Bayesian CT reconstruction method using Markov random fields with adaptive hyperparameter estimation, outperforming traditional FBP in adverse imaging scenarios.

Findings

Proposed algorithm outperforms FBP in low-dose conditions.

Adaptive hyperparameter estimation improves reconstruction quality.

Algorithm broadens CT applicability in dose-sensitive and time-constrained settings.

Abstract

X-ray computed tomography (CT) reveals the materials' internal structures non-destructively from a tilt series of projected images. Filtered back projection (FBP) is a widely-adopted reconstruction algorithm in CT owing to its small computational cost. Under low-dose or sparse-view conditions, however, FBP often amplifies noise, severely degrading the reconstructed images. In this study, we evaluated the performance of a Bayesian CT reconstruction algorithm based on the Markov random field model under such adverse conditions. Through simulations, we demonstrated that the proposed algorithm shows higher reconstruction performance than FBP under both low-dose and sparse-view conditions. The hyperparameters are estimated by minimizing the Bayesian free energy, enabling adaptive reconstruction that reflects the noise characteristics of the observed projection data. These results suggest that the proposed algorithm can broaden the applicability of CT to dose-sensitive applications and time-constrained measurements, where only limited observed projection data are available.